article

Anomaly detection using manifold embedding and its applications in transportation corridors

Authors:

Amrudin Agovic,

Arindam Banerjee,

Auroop Ganguly,

Vladimir ProtopopescuAuthors Info & Claims

Intelligent Data Analysis, Volume 13, Issue 3

Pages 435 - 455

Published: 01 August 2009 Publication History

Abstract

The formation of secure transportation corridors, where cargoes and shipments from points of entry can be dispatched safely to highly sensitive and secure locations, is a high national priority. One of the key tasks of the program is the detection of anomalous cargo based on sensor readings in truck weigh stations. Due to the high variability, dimensionality, and/or noise content of sensor data in transportation corridors, appropriate feature representation is crucial to the success of anomaly detection methods in this domain. In this paper, we empirically investigate the usefulness of manifold embedding methods for feature representation in anomaly detection problems in the domain of transportation corridors. We focus on both linear methods, such as multi-dimensional scaling (MDS), as well as nonlinear methods, such as locally linear embedding (LLE) and isometric feature mapping (ISOMAP). Our study indicates that such embedding methods provide a natural mechanism for keeping anomalous points away from the dense/normal regions in the embedding of the data. We illustrate the efficacy of manifold embedding methods for anomaly detection through experiments on simulated data as well as real truck data from weigh stations.

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Cited By

Murthy KGhosh A(2017)Noisy-free Length Discriminant Analysis with cosine hyperbolic framework for dimensionality reductionExpert Systems with Applications: An International Journal10.1016/j.eswa.2017.03.03481:C(88-107)Online publication date: 15-Sep-2017
https://dl.acm.org/doi/10.1016/j.eswa.2017.03.034
Chen TTang LSun YChen ZZhang K(2016)Entity embedding-based anomaly detection for heterogeneous categorical eventsProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3060621.3060815(1396-1403)Online publication date: 9-Jul-2016
https://dl.acm.org/doi/10.5555/3060621.3060815
Fadlil JPao HLee YAkoglu LMüller EVreeken J(2013)Anomaly detection on ITS data via view associationProceedings of the ACM SIGKDD Workshop on Outlier Detection and Description10.1145/2500853.2500859(22-30)Online publication date: 11-Aug-2013
https://dl.acm.org/doi/10.1145/2500853.2500859
Show More Cited By

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Anomaly detection using manifold embedding and its applications in transportation corridors
1. Computing methodologies
  1. Machine learning

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cover image Intelligent Data Analysis

Intelligent Data Analysis Volume 13, Issue 3

Knowledge Discovery from Data Streams

August 2009

92 pages

ISSN:1088-467X

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IOS Press

Netherlands

Publication History

Published: 01 August 2009

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Cited By

Murthy KGhosh A(2017)Noisy-free Length Discriminant Analysis with cosine hyperbolic framework for dimensionality reductionExpert Systems with Applications: An International Journal10.1016/j.eswa.2017.03.03481:C(88-107)Online publication date: 15-Sep-2017
https://dl.acm.org/doi/10.1016/j.eswa.2017.03.034
Chen TTang LSun YChen ZZhang K(2016)Entity embedding-based anomaly detection for heterogeneous categorical eventsProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3060621.3060815(1396-1403)Online publication date: 9-Jul-2016
https://dl.acm.org/doi/10.5555/3060621.3060815
Fadlil JPao HLee YAkoglu LMüller EVreeken J(2013)Anomaly detection on ITS data via view associationProceedings of the ACM SIGKDD Workshop on Outlier Detection and Description10.1145/2500853.2500859(22-30)Online publication date: 11-Aug-2013
https://dl.acm.org/doi/10.1145/2500853.2500859
Pellissier MMartin HKotsakis EGuerrini G(2013)Mining irregularities in maritime container itinerariesProceedings of the Joint EDBT/ICDT 2013 Workshops10.1145/2457317.2457325(45-51)Online publication date: 18-Mar-2013
https://dl.acm.org/doi/10.1145/2457317.2457325

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